We analyze the ordinal structure of long‐range dependent time series. To this end, we use so called ordinal patterns which describe the relative position of consecutive data points. We provide two estimators for the probabilities of ordinal patterns and prove limit theorems in different settings, namely stationarity and (less restrictive) stationary increments. In the second setting, we encounter a Rosenblatt distribution in the limit. We prove more general limit theorems for functions with Hermite rank 1 and 2. We derive the limit distribution for an estimation of the Hurst parameter H if it is higher than 3/4. Thus, our theorems complement results for lower values of H which can be found in the literature. Finally, we provide some simulations that illustrate our theoretical results.

Let (X k ) k≥1 be a Gaussian long-range dependent process with EX 1 = 0, EX 2 1 = 1 and covariance function r(We study the asymptotic behaviour of the associated sequential empirical process (R N (x, t)) with respect to a weighted sup-norm · w . We show that, after an appropriate normalization, (R N (x, t)) converges weakly in the space of càdlàg functions with finite weighted norm to a Hermite process.

Let (X j ) j≥1 be a multivariate long-range dependent Gaussian process. We study the asymptotic behavior of the corresponding sequential empirical process indexed by a class of functions. If some entropy condition is satisfied we have weak convergence to a linear combination of Hermite processes.

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